Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry
Using probabilistic relational learning to support bronchial carcinoma diagnosis based on ion mobility spectrometry
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DOI：
10.1007/s12127-010-0042-9
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发表时间：
2010-06-01
影响因子：
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通讯作者：
Baumbach, Joerg Ingo
中科院分区：
文献类型：
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作者：
Finthammer, Marc;Beierle, Christoph;Baumbach, Joerg Ingo
离子迁移谱（IMS）提供了一种分析人呼出的物质的方法。在本文中，我们报告的方法，以支持早期诊断支气管癌的基础上，这样的IMS测量。给定一组离子迁移谱中的峰，我们首先用改进的kmeans算法对这些峰进行聚类。然后，我们应用概率关系建模和学习方法的逻辑表示的离子迁移率谱和峰簇获得的数据。马尔可夫逻辑网络和MLN系统Alchemy用于各种建模和学习场景。这些方案进行评估方面的易用性，分类精度和知识表示方面。
Ion Mobility Spectrometry (IMS) provides a means for analyzing the substances a person exhales. In this paper, we report on an approach to support early diagnosis of bronchial carcinoma based on such IMS measurements. Given the peaks in a set of ion mobility spectra, we first cluster these peaks with a modified kmeans algorithm. We then apply probabilistic relational modelling and learning methods to a logical representation of the data obtained from the ion mobility spectra and the peak clusters. Markov Logic Networks and the MLN system Alchemy are employed for various modelling and learning scenarios. These scenarios are evaluated with respect to ease of use, classification accuracy, and knowledge representation aspects.